Demo Data from EDBT 2026 / reviewers in the wild / expert
S. H. Henry Phang
dblp:88/2683
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2001
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Multi-agent systems · 44% Kernel, tree and ensemble methods · 44% Legged, aerial and field robots · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Kernel, tree and ensemble methods › ensemble learning
decision fusion |
0.0 | 1 | 2001 | Multilevel Multisensor Based Decision Fusion for Intelligent Animal Robot · ICRA 2001 |
Knowledge, reasoning and agents › Multi-agent systems
multi-robot coordination |
0.0 | 1 | 2001 | Multilevel Multisensor Based Decision Fusion for Intelligent Animal Robot · ICRA 2001 |
Robotics › Legged, aerial and field robots
legged robots |
0.0 | 1 | 2001 | Multilevel Multisensor Based Decision Fusion for Intelligent Animal Robot · ICRA 2001 |
Methods — techniques the papers use, named apart from their topics
weight functions · 0.0rule-based fusion · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2001 | Multilevel Multisensor Based Decision Fusion for Intelligent Animal RobotabstractThe objective of the paper is to develop multilevel multisensor based decision fusion principles for an intelligent animal robot. The paper presents the design and implementation of a four-legged animal robot. The animal robot's head, body, and legs were constructed using aluminum material. The control/decision unit, driver unit and sensory unit were designed for the control of the animal robot. In addition, we use hierarchically organized control hardware and multilevel multisensor based fusion techniques to obtain a fused decision. Weight function and rule based algorithms are employed to fuse and integrate multisensor data. Preliminary experimental results indicate that the proposed methods can effectively fuse decisions for the animal robot. Ren C. Luo, S. H. Henry Phang, Kuo Lan Su |
ICRA | 2 |